1	Announcements Panorama artifacts will be posted online
2	Light Readings Andrew Glassner, Principles of Digital Image Synthesis (Vol. 1), Morgan Kaufmann Publishers, 1995, pp. 5-32. Watt & Policarpo, The Computer Image, Addison-Wesley, 1998, pp. 64-71, 103-114 (5.3 is optional).
3	Properties of light Today What is light? How do we measure it? Next time How does light propagate? How does light interact with matter?
4	What is light?
5	The light field Known as the plenoptic function If you know R, you can predict how the scene would appear from any viewpoint. How?
6	Stanford light field gantry
7	More info on light fields If you’re interested to read more: The plenoptic function Original reference: E. Adelson and J. Bergen, "The Plenoptic Function and the Elements of Early Vision," in M. Landy and J. A. Movshon, (eds) Computational Models of Visual Processing, MIT Press 1991. L. McMillan and G. Bishop, “Plenoptic Modeling: An Image-Based Rendering System”, Proc. SIGGRAPH, 1995, pp. 39-46. The light field M. Levoy and P. Hanrahan, “Light Field Rendering”, Proc SIGGRAPH 96, pp. 31-42. S. J. Gortler, R. Grzeszczuk, R. Szeliski, and M. F. Cohen, "The lumigraph," in Proc. SIGGRAPH, 1996, pp. 43-54.
8	What is light?
9	The visible light spectrum We “see” electromagnetic radiation in a range of wavelengths
10	Light spectrum The appearance of light depends on its power spectrum How much power (or energy) at each wavelength
11	The human visual system Color perception Light hits the retina, which contains photosensitive cells
12	Density of rods and cones Rods and cones are non-uniformly distributed on the retina Rods responsible for intensity, cones responsible for color Fovea - Small region (1 or 2°) at the center of the visual field containing the highest density of cones (and no rods). Less visual acuity in the periphery—many rods wired to the same neuron
13	Demonstrations of visual acuity
14	Demonstrations of visual acuity
15	Brightness contrast and constancy The apparent brightness depends on the surrounding region brightness contrast: a constant colored region seem lighter or darker depending on the surround: http://www.sandlotscience.com/Contrast/Contrast_frm.htm brightness constancy: a surface looks the same under widely varying lighting conditions.
16	Light response is nonlinear Our visual system has a large dynamic range We can resolve both light and dark things at the same time One mechanism for achieving this is that we sense light intensity on a logarithmic scale an exponential intensity ramp will be seen as a linear ramp Another mechanism is adaptation rods and cones adapt to be more sensitive in low light, less sensitive in bright light.
17	Visual dynamic range
18	After images Tired photoreceptors Send out negative response after a strong stimulus
19	Color perception Three types of cones Each is sensitive in a different region of the spectrum but regions overlap Short (S) corresponds to blue Medium (M) corresponds to green Long (L) corresponds to red Different sensitivities: we are more sensitive to green than red Colorblindness—deficiency in at least one type of cone
20	Color perception Rods and cones act as filters on the spectrum To get the output of a filter, multiply its response curve by the spectrum, integrate over all wavelengths Each cone yields one number Q: How can we represent an entire spectrum with 3 numbers?
21	Perception summary The mapping from radiance to perceived color is quite complex! We throw away most of the data We apply a logarithm Brightness affected by pupil size Brightness contrast and constancy effects Afterimages
22	Camera response function Now how about the mapping from radiance to pixels? It’s also complex, but better understood This mapping known as the film or camera response function
23	Recovering the camera response Method 1 Carefully model every step in the pipeline measure aperture, model film, digitizer, etc. this is really hard to get right Method 2 Calibrate (estimate) the response function Image several objects with known radiance Measure the pixel values Fit a function Find the inverse: maps pixel intensity to radiance
24	Recovering the camera response Method 3 Calibrate the response function from several images Consider taking images with shutter speeds 1/1000, 1/100, 1/10, and 1 Q: What is the relationship between the radiance or pixel values in consecutive images? A: 10 times as much radiance Can use this to recover the camera response function
25	High dynamic range imaging Techniques Debevec: http://www.debevec.org/Research/HDR/ Columbia: http://www.cs.columbia.edu/CAVE/tomoo/RRHomePage/rrgallery.html
26	Light transport
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29	The interaction of light and matter What happens when a light ray hits a point on an object? Some of the light gets absorbed converted to other forms of energy (e.g., heat) Some gets transmitted through the object possibly bent, through “refraction” Some gets reflected as we saw before, it could be reflected in multiple directions at once Let’s consider the case of reflection in detail In the most general case, a single incoming ray could be reflected in all directions. How can we describe the amount of light reflected in each direction?
30	The BRDF The Bidirectional Reflection Distribution Function Given an incoming ray and outgoing ray what proportion of the incoming light is reflected along this ray?
31	Diffuse reflection Diffuse reflection Dull, matte surfaces like chalk or latex paint Microfacets scatter incoming light randomly Effect is that light is reflected equally in all directions
32	Diffuse reflection
33	Specular reflection
34	Phong illumination model Phong approximation of surface reflectance Assume reflectance is modeled by three components Diffuse term Specular term Ambient term (to compensate for inter-reflected light)
35	Measuring the BRDF Gonioreflectometer Device for capturing the BRDF by moving a camera + light source Need careful control of illumination, environment
36	Columbia-Utrecht Database Captured BRDF models for a variety of materials http://www.cs.columbia.edu/CAVE/curet/.index.html
37	Advanced topics Ongoing research in BRDF’s seeks to: Recover BRDF’s from “just a few” images, model global light transport Yu, Debevec, Malik and Hawkins, “Inverse Global Illumination”, SIGGRAPH 1999. Model semi-transparent, refractive surfaces Zongker, Werner, Curless, and Salesin, “Environment Matting and Compositing”, SIGGRAPH 99, pp. 205-214. Model sub-surface scattering Jensen, Marschner, Levoy and Hanrahan: “A Practical Model for Subsurface Light Transport”, SIGGRAPH'2001.